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DTSTART;TZID=America/Los_Angeles:20250624T133000
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UID:dac_DAC 2025_sess126_RESEARCH034@linklings.com
SUMMARY:SAPO: Improving the Scalability and Accuracy of Quantum Linear Sol
 ver for Portfolio Optimization
DESCRIPTION:Tianze Zhu, Liqiang Lu, and Jiajun Chen (Zhejiang University);
  Yuhang Chen (Peking University); Hengrui Chen, Meng Xi, and Jinshan Zhang
  (Zhejiang University); Xiaoming SUN (Institute of Computing Technology, C
 hinese Academy of Sciences); and Jianwei Yin (Zhejiang University)\n\nPort
 folio optimization problem stands as one of the most important financial s
 ervice, which suffers from huge computational pressure due to arithmetic c
 omplexity. While, quantum computing has developed rapidly over the last fe
 w decades, offering polynomial or even exponential speedup that turns out 
 to be a promising approach. However,existing quantum methods is fundamenta
 lly limited by either poor scalability or insufficient accuracy.\nIn this 
 paper, we propose SAPO, which formally articulates the quantum circuit tha
 t seamlessly integrates financial theory and historical data characteristi
 cs with quantum algebra. \nThe circuit design is extended from the HHL alg
 orithm incorporating mean-variance theory, which promotes scalability by e
 quivalent transformation. Then, we present a min-max eigenvalue model that
  leverages historical financial information to refine parameter settings w
 ith high accuracy. \nExperiments conducted on stock market data demonstrat
 e that SAPO can effectively reduce the complexity by 36.94% compared to ba
 sic HHL and improve the accuracy by 1.46X compared to hybrid HHL.\n\nTopic
 s: Design\n\nTracks: DES6: Quantum Computing\n\nSession Chairs: Saurabh Ko
 tiyal (Altera) and Sanjaya Lohani (Southern Methodist University)\n\n
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